Fast independent component analysis in kernel feature spaces

Andràs Kocsor, Jànos Csirik

Research output: Conference contribution

13 Citations (Scopus)

Abstract

It is common practice to apply linear or nonlinear feature extraction methods before classification. Usually linear methods are faster and simpler than nonlinear ones but an idea successfully employed in the nonlinearization of Support Vector Machines permits a simple and effective extension of several statistical methods to their nonlinear counterparts. In this paper we follow this general nonlinearization approach in the context of Independent Component Analysis, which is a general purpose statistical method for blind source separation and feature extraction. In addition, nonlinearized formulae are furnished along with an illustration of the usefulness of the proposed method as an unsupervised feature extractor for the classification of Hungarian phonemes.

Original languageEnglish
Title of host publicationSOFSEM 2001
Subtitle of host publicationTheory and Practice of Informatics - 28th Conference on Current Trends in Theory and Practice of Informatics, Proceedings
EditorsLeszek Pacholski, Peter Ruzicka
PublisherSpringer Verlag
Pages271-281
Number of pages11
ISBN (Print)9783540429128
DOIs
Publication statusPublished - jan. 1 2001
Event28th International Conference on Current Trends in Theory and Practice of Informatics, SOFSEM 2001 - Piestany, Slovakia
Duration: nov. 24 2001dec. 1 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2234
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other28th International Conference on Current Trends in Theory and Practice of Informatics, SOFSEM 2001
CountrySlovakia
CityPiestany
Period11/24/0112/1/01

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Kocsor, A., & Csirik, J. (2001). Fast independent component analysis in kernel feature spaces. In L. Pacholski, & P. Ruzicka (Eds.), SOFSEM 2001: Theory and Practice of Informatics - 28th Conference on Current Trends in Theory and Practice of Informatics, Proceedings (pp. 271-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2234). Springer Verlag. https://doi.org/10.1007/3-540-45627-9_24